title: Using whole disease modeling to inform resource allocation decisions: economic evaluation of a clinical guideline for colorectal cancer using a single model.
abstract: OBJECTIVE
  To assess the feasibility and value of simulating whole disease and treatment pathways within a single model to provide a common economic basis for informing resource allocation decisions.
  
  
  METHODS
  A patient-level simulation model was developed with the intention of being capable of evaluating multiple topics within National Institute for Health and Clinical Excellence's colorectal cancer clinical guideline. The model simulates disease and treatment pathways from preclinical disease through to detection, diagnosis, adjuvant/neoadjuvant treatments, follow-up, curative/palliative treatments for metastases, supportive care, and eventual death. The model parameters were informed by meta-analyses, randomized trials, observational studies, health utility studies, audit data, costing sources, and expert opinion. Unobservable natural history parameters were calibrated against external data using Bayesian Markov chain Monte Carlo methods. Economic analysis was undertaken using conventional cost-utility decision rules within each guideline topic and constrained maximization rules across multiple topics.
  
  
  RESULTS
  Under usual processes for guideline development, piecewise economic modeling would have been used to evaluate between one and three topics. The Whole Disease Model was capable of evaluating 11 of 15 guideline topics, ranging from alternative diagnostic technologies through to treatments for metastatic disease. The constrained maximization analysis identified a configuration of colorectal services that is expected to maximize quality-adjusted life-year gains without exceeding current expenditure levels.
  
  
  CONCLUSIONS
  This study indicates that Whole Disease Model development is feasible and can allow for the economic analysis of most interventions across a disease service within a consistent conceptual and mathematical infrastructure. This disease-level modeling approach may be of particular value in providing an economic basis to support other clinical guidelines.
authors.affiliation: Health Economics and Decision Science, School of Health and Related Research, University of Sheffield, Sheffield, UK. p.tappenden@sheffield.ac.uk
authors.forename: Paul
authors.initials: P
authors.lastname: Tappenden
authors.forename: Jim
authors.initials: J
authors.lastname: Chilcott
authors.forename: Alan
authors.initials: A
authors.lastname: Brennan
authors.forename: Hazel
authors.initials: H
authors.lastname: Squires
authors.forename: Rob
authors.initials: R
authors.lastname: Glynne-Jones
authors.forename: Janine
authors.initials: J
authors.lastname: Tappenden
mesh_terms: D001499:Bayes Theorem; D015179:Colorectal Neoplasms / Q000175:diagnosis / Q000191:economics* / Q000628:therapy; D003198:Computer Simulation; D003362:Cost-Benefit Analysis; D003657:Decision Making; D005240:Feasibility Studies; D015276:Health Care Rationing / Q000191:economics*; D006801:Humans; D008390:Markov Chains; D018803:Models, Economic; D009010:Monte Carlo Method; D017410:Practice Guidelines as Topic; D019057:Quality-Adjusted Life Years; D016032:Randomized Controlled Trials as Topic